Python extension for computing string edit distances and similarities.
Project description
Levenshtein
Introduction
The Levenshtein Python C extension module contains functions for fast computation of:
- Levenshtein (edit) distance, and edit operations
- string similarity
- approximate median strings, and generally string averaging
- string sequence and set similarity
This is a fork of ztane/python-Levenshtein, since the original project is no longer actively maintained.
Requirements
- Python 2.7 or later
Installation
pip install levenshtein
Documentation
The documentation for the current version can be found at https://maxbachmann.github.io/Levenshtein/
License
Levenshtein is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version.
See the file COPYING for the full text of GNU General Public License version 2.
Changelog
v0.16.0
- Add support for Python 3.10
v0.15.0
- Update SequenceMatcher interface to support the autojunk parameter
v0.14.0
- Drop Python 2 support
- Fixed free of non heap object due caused by zero offset on a heap object
- Fixed warnings about missing type conversions
- Fix segmentation fault in subtract_edit when incorrect input types are used
- Fixed unchecked memory allocations
- Implement distance/ratio/hamming/jaro/jaro_winkler using rapidfuzz instead of providing a own implementation
- Implement Wrapper for inverse/editops/opcodes/matching_blocks/subtract_edit/apply_edit using Cython to simplify support for new Python versions
v0.13.0
- Maintainership passed to Max Bachmann
- use faster bitparallel implementations for distance and ratio
- avoid string copies in distance, ratio and hamming
- Fix usage of deprecated Unicode APIs in distance, ratio and hamming
- Fixed incorrect window size inside Jaro and Jaro-Winkler implementation
- Fixed incorrect exception messages
- Removed unused functions and compiler specific hacks
- Split the Python and C implementations to simplify building of the C library
- Fixed multiple bugs which prevented the use as C library, since some functions only got defined when compiling for Python
- Build and deliver python wheels for the library
- Fixed incorrect allocation size in lev_editops_matching_blocks and lev_opcodes_matching_blocks
v0.12.1
- Fixed handling of numerous possible wraparounds in calculating the size of memory allocations; incorrect handling of which could cause denial of service or even possible remote code execution in previous versions of the library.
v0.12.0
- Fixed a bug in StringMatcher.StringMatcher.get_matching_blocks /
extract_editops for Python 3; now allow only
str
editops on both Python 2 and Python 3, for simpler and working code. - Added documentation in the source distribution and in GIT
- Fixed the package layout: renamed the .so/.dll to _levenshtein, and made it reside inside a package, along with the StringMatcher class.
- Fixed spelling errors.
v0.11.2
-
Fixed a bug in setup.py: installation would fail on Python 3 if the locale did not specify UTF-8 charset (Felix Yan).
-
Added COPYING, StringMatcher.py, gendoc.sh and NEWS in MANIFEST.in, as they were missing from source distributions.
v0.11.1
- Added Levenshtein.h to MANIFEST.in
v0.11.0
- Python 3 support, maintainership passed to Antti Haapala
v0.10.2
- Made python-Lehvenstein Git compatible and use setuptools for PyPi upload
- Created HISTORY.txt and made README reST compatible
v0.10.1
- apply_edit() broken for Unicodes was fixed (thanks to Radovan Garabik)
- subtract_edit() function was added
v0.10.0
- Hamming distance, Jaro similarity metric and Jaro-Winkler similarity metric were added
- ValueErrors raised on wrong argument types were fixed to TypeErrors
v0.9.0
- a poor-but-fast generalized median method quickmedian() was added
- some auxiliary functions added to the C api (lev_set_median_index, lev_editops_normalize, ...)
v0.8.2
- fixed missing `static' in the method list
v0.8.1
- some compilation problems with non-gcc were fixed
v0.8.0
- median_improve(), a generalized median improving function, was added
- an arbitrary length limitation imposed on greedy median() result was removed
- out of memory should be handled more gracefully (on systems w/o memory overcomitting)
- the documentation now passes doctest
v0.7.0
- fixed greedy median() for Unicode characters > U+FFFF, it's now usable with whatever integer type wchar_t happens to be
- added missing MANIFEST
- renamed exported C functions, all public names now have lev_, LEV_ or Lev prefix; defined lev_byte, lev_wchar, and otherwise santinized the (still unstable) C interface
- added edit-ops group of functions, with two interfaces: native, useful for string averaging, and difflib-like for interoperability
- added an example SequenceMatcher-like class StringMatcher
v0.6.0
- a segfault in seqratio()/setratio() on invalid input has been fixed to an exception
- optimized ratio() and distance() (about 20%)
- Levenshtein.h header file was added to make it easier to actually use it as a C library
v0.5.0
- a segfault in setratio() was fixed
- median() handles all empty strings situation more gracefully
v0.4.0
- new functions seqratio() and setratio() computing similarity between string sequences and sets
- Levenshtein optimizations (affects all routines except median())
- all Sequence objects are accepted, not just Lists
v0.3.0
- setmedian() finding set median was added
- median() initial overhead for Unicodes was reduced
v0.2.0
- ratio() and distance() now accept both Strings and Unicodes
- removed uratio() and udistance()
- Levenshtein.c is now compilable as a C library (with -DNO_PYTHON)
- a median() function finding approximate weighted median of a string set was added
v0.1.0
- Inital release
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SHA256 | bac8762ccaa5cdb062310b49b21ec912beaf8968a4da3fd561cc3baa227754a8 |
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MD5 | 6ca943c7d3d25ce02b114ae54ccf4491 |
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BLAKE2b-256 | 4df8215bbdeb785233635c1e81ac071adeba41483c021da13b18a06156ae553c |
Hashes for Levenshtein-0.16.0-cp36-cp36m-win_amd64.whl
Algorithm | Hash digest | |
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SHA256 | e5beda373da7405468b66eccf58206767ce4c241cebb55b21a2c3261436c6646 |
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MD5 | 942b2eb5e21da6f90a4d3d3e731f823e |
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BLAKE2b-256 | bc33825a24546435c9c77fd5d24a1fd985d2d38b38f38742254a58162a34171e |
Hashes for Levenshtein-0.16.0-cp36-cp36m-win32.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | e79e0de22c1497f9de874394bb1c0b59bcbfd18a873e0de94188718ef8399155 |
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MD5 | 10f4dae67530ef9e07cc93f1a66e3aea |
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BLAKE2b-256 | 62cc137d35c9a8d267284e1f80bfc368bd7a2ea78b81d54beccfbf12bb84ccb2 |
Hashes for Levenshtein-0.16.0-cp36-cp36m-manylinux_2_17_s390x.manylinux2014_s390x.whl
Algorithm | Hash digest | |
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SHA256 | cc3b2ee8c7c33e23d18574ba95c2b7448e08c8ef4b6675a27cf9d1077fc4eda5 |
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MD5 | a4a1ea100183fe3b62274f67e720320b |
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BLAKE2b-256 | 5bcc58d766326d99cbcf25107b0706f7d143280ba9015307342a93ac9328704e |
Hashes for Levenshtein-0.16.0-cp36-cp36m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 2fff380979dc2e7bda5f8a43b76263bdfb289371b38099b5927be0bace1b1fda |
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MD5 | 28ebcc598f3c6e6d69a20c815c5f17f8 |
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BLAKE2b-256 | e6d8f39a24190b3ebf20f95a09655a98d44fc4c787880b071f11ead2403e0ee4 |
Hashes for Levenshtein-0.16.0-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 9cc997fd24cd67572d8ed719f306896477cafb0fd2d13ac3ff9d3cffa3be9803 |
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MD5 | 45323d2ce0ca30e276c40fecb06ded4b |
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BLAKE2b-256 | 7d19e6e6d9374c28acbee3435dfd0d93014ffead634cc82affd64f1a7a0acc1a |
Hashes for Levenshtein-0.16.0-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | b651c2336eab31dee2f2f422ea619e5d3b452ba4fe6b10aeea205935b02fb51b |
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MD5 | dc43ec0f49840bef3b26f2ab3059220f |
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BLAKE2b-256 | ac9a7ba434b06214b14bdddceaa68153733ea0dd147bd6f05a816b72ffdfa5b4 |
Hashes for Levenshtein-0.16.0-cp36-cp36m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_12_i686.manylinux2010_i686.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | d54286dad923ab2b3954794c9cb6a10e123ad67597a86d4d383fe753e8fee314 |
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MD5 | be3740feb308d60267aa96816dfd3584 |
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BLAKE2b-256 | 72ecf4b21a6ed72979558b3aa51afa0bd79ec7518d3c456a9152530cab2c4d3e |
Hashes for Levenshtein-0.16.0-cp36-cp36m-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | d8cac37456ef4d1fa23b83958bfed2bf09f9228e62fa06ec24bb7f76da129e95 |
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MD5 | 8d60adf8187774a2cf275fb970ac2de8 |
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BLAKE2b-256 | ee8887b929c800b31eaca48999eac16510c01edf35618755c86021fe040c55e9 |
Hashes for Levenshtein-0.16.0-cp35-cp35m-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 9901ea03c7aec5ca497a16c91a4aa8cff4c5dd72eac9cf457e66cde307a3e91e |
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MD5 | 3ed7508bc7e72284cd865315e85dbc6e |
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BLAKE2b-256 | fb22e4d869594750d608965242b36fecc77ff3d0b72bd05de4af40355c8923cc |
Hashes for Levenshtein-0.16.0-cp35-cp35m-win32.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | fdd09d21a5a4793c320a852a99e4484b716cf54beaf622004f9ebdbed673c591 |
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MD5 | dcf9a63f38b767b26d267f4c364ca363 |
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BLAKE2b-256 | dfc7d19c71341fbcdd724807dbd4594494d9fd9df2c14d035cc2a3874265e18a |
Hashes for Levenshtein-0.16.0-cp35-cp35m-manylinux2010_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 89d5e91722ca1efa0fa7fc5483d264df09237d472bda0a782667211bf9fdc5cd |
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MD5 | 999e760de3117aa0673c9f83d2662082 |
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BLAKE2b-256 | d00104da3e10b1084ebc8c5ffb6e6efa923b61a11661343c1b37f1a00a9fdf89 |
Hashes for Levenshtein-0.16.0-cp35-cp35m-manylinux2010_i686.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 1f3ef34555a3f5fe92ad12073a241d6b921a44f03e2a50ee733926df56582a55 |
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MD5 | 9337f797d4ea0e99b80d5213b88849d1 |
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BLAKE2b-256 | ce1fd3a1ffa75807a75b63daaf44ecde8d36c1e351df1eb27d16072a1fd5c204 |
Hashes for Levenshtein-0.16.0-cp35-cp35m-manylinux1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 1cfdf50304b3d8454ce0bb662e3c7810997d599fc5f232ed3ad4effd4a3505b5 |
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MD5 | 448f5eda0f4382cdcd7a866f9d5e7b65 |
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BLAKE2b-256 | 32980eb59535fee1bdf1e6b74f86bbb4e6681fdcfe207a0e2c79c1d5a7608133 |
Hashes for Levenshtein-0.16.0-cp35-cp35m-manylinux1_i686.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | b3130721ccf4817384f22ea335605dc49d65ee7c78d7dd33dc09fe55d3212f35 |
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MD5 | 367a2abfa21ebe5a09d9bf3c8731fcee |
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BLAKE2b-256 | 7c694622c1749a0d9d1bb8fed2d29e922ab855ee39524bf0a8905ddec211aa60 |
Hashes for Levenshtein-0.16.0-cp35-cp35m-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | a9d0f5f61d4cb55351aa92d2815b985e4f8fbdae257a08352e43d81ce68416be |
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MD5 | 58c299c7f4762e9d2eaae517db934080 |
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BLAKE2b-256 | e6906969bd77a0068a4f9b0ce908883ed2d2d1f98e30ea46e60999a236a0e3c2 |